https://scholars.lib.ntu.edu.tw/handle/123456789/558974
標題: | Meta Learning for End-To-End Low-Resource Speech Recognition | 作者: | Hsu, J.-Y. Chen, Y.-J. HUNG-YI LEE |
關鍵字: | IARPA-BABEL; language adaptation; low-resource; meta-learning; speech recognition | 公開日期: | 2020 | 卷: | 2020-May | 起(迄)頁: | 7844-7848 | 來源出版物: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | 摘要: | In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to achieve fast adaptation on unseen target language, via recently proposed model-agnostic meta learning algorithm (MAML). We evaluated the proposed approach using six languages as pretraining tasks and four languages as target tasks. Preliminary results showed that the proposed method, MetaASR, significantly outperforms the state-of-the-art multitask pretraining approach on all target languages with different combinations of pretraining languages. In addition, since MAML's model-agnostic property, this paper also opens new research direction of applying meta learning to more speech-related applications. © 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85089208969&partnerID=40&md5=c3fcd2a227e6e220c84cc6de30f7ab61 https://scholars.lib.ntu.edu.tw/handle/123456789/558974 |
ISSN: | 15206149 | DOI: | 10.1109/ICASSP40776.2020.9053112 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。